Instance Normalisation.
In the case of motion segmentation,
objects can only be detected if they undergo an independent motion from the camera;
thus previous work attempts to compensate for camera motion [1]}, [2]}.
We are inspired by these ideas, but instead of explicitly estimating homography or camera motion,
we take a poor-man's approach by simply using Instance Normalisation (IN) [3]}
in the CNN encoder and decoder,
which normalizes each channel in each training sample independently.
Intuitively, the mean activation tends to be dominated by the motions in the large homogeneous region,
which is usually the background.
This normalization, in combination with ReLU activations,
helps gradually separate the background motion from the foreground motions.
